Artificial intelligence in interventional cardiology transforming diagnosis, decision-making and procedural precision
Sujeet Narain, Vinal J. Shah, Shubham SharmaAbstract:
BACKGROUND:
Cardiovascular disease remains the leading cause of mortality globally, imposing an immense burden on healthcare systems. The rapid expansion of artificial intelligence (AI) technologies encompassing machine learning, deep learning (DL) and neural network architectures offers unprecedented potential to reshape how interventional cardiologists diagnose, plan and execute procedures.
OBJECTIVES:
This state-of-the-art review synthesises current evidence on the clinical deployment of AI across the spectrum of interventional cardiology, including coronary imaging, physiological assessment, procedural guidance, transcatheter structural interventions and post-procedural outcomes prediction.
METHODS:
A comprehensive synthesis of peer-reviewed literature published from 2019 to 2025 was performed, drawing from
RESULTS:
AI has demonstrated transformative capabilities in automated coronary angiography analysis, non-invasive fractional flow reserve derivation, intravascular imaging interpretation, transcatheter aortic valve replacement/transcatheter aortic valve implantation planning, robotic-assisted percutaneous coronary intervention and adverse-event prediction. DL models now achieve diagnostic accuracy rivalling expert interventionalists in controlled settings, with area-under-curve values exceeding 0.93 for haemodynamic significance classification.
CONCLUSIONS:
AI holds extraordinary promise for augmenting human expertise in the catheterisation laboratory. However, broad clinical translation remains constrained by algorithmic opacity, limited prospective validation, regulatory uncertainty and concerns about training-data bias. A structured roadmap integrating rigorous trial design, equitable data collection and transparent regulatory pathways is essential for responsible deployment.